PROJECT SUMMARY Over 2 million children and young adults worldwide were perinatally infected with HIV, and these children are at risk of many adverse health outcomes, including a high risk of cervical and other human papillomavirus (HPV)-associated cancers. Unfortunately, the Pediatric HIV/AIDS Cohort Study, a rich source of observational data on children with perinatal HIV infection, recently found troubling evidence that suggests low efficacy of the HPV vaccine in children with perinatal HIV infection. Given the high vulnerability of women and girls with HIV to cervical cancer, this finding needs to be explored. However, since the youth in this cohort are still relatively young assessing the impact of vaccination on cervical cancer in the data is difficult. We propose to answer key clinical questions about cervical cancer prevention in girls and young women with perinatal HIV infection by combining this observational data with a well-validated individual-level simulation model of cervical cancer. We will estimate the vaccine efficacy based on number of doses, age, and HIV disease severity at vaccination from the Pediatric HIV/AIDS Cohort Study data. We will calibrate the simulation model to vaccine efficacy in the cohort study, and assess assumptions required to estimate causal effects from this model. Finally, we will project the impact of novel vaccination strategies, such as earlier age at initiation and additional doses, on cervical cancer incidence in young women with perinatal HIV infection to determine whether altering the recommendations for HPV vaccination can reduce cervical cancer risk in this group. Our work will implement and extend cutting-edge approaches to emulate randomized trials using observational data and combine this with simulation models to provide rapid generation of high-quality evidence to inform decision-making. The results of this project will improve the health and quality of life of young women with perinatal HIV-infection in the United States and worldwide and demonstrate the implementation of a novel method for estimating causal effects when randomized trials and observational studies have insufficient data to provide evidence-based decision-making.